




'''simple docstring'''

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


a    :							Tuple										=    pytest.mark.integration






@require_faiss
class 		a							(  _UpperCAmelCase	):


				def     A_					(						self       :   int   ):
									snake_case_		     =							Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(lowerCAmelCase__   ) for x in np.arange(30   ).tolist()]}   )
									return dset


				def     A_					(						self       :   int   ):
									import faiss

									snake_case_		     =							self._create_dummy_dataset()
									snake_case_		     =							dset.map(
									    lambda lowercase_			,							lowercase_   : {"vecs": i * np.ones(5			,							dtype=np.floataa   )}			,							with_indices=lowerCAmelCase__			,							keep_in_memory=lowerCAmelCase__   )
									snake_case_		     =							dset.add_faiss_index('''vecs'''			,							batch_size=100			,							metric_type=faiss.METRIC_INNER_PRODUCT   )
									snake_case_     ,snake_case_		     =							dset.get_nearest_examples('''vecs'''			,							np.ones(5			,							dtype=np.floataa   )   )
									self.assertEqual(examples['''filename'''][0]			,							'''my_name-train_29'''   )
									dset.drop_index('''vecs'''   )


				def     A_					(						self       :   List[str]   ):
									import faiss

									snake_case_		     =							self._create_dummy_dataset()
									dset.add_faiss_index_from_external_arrays(
									    external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1			,							1   )			,							index_name='''vecs'''			,							batch_size=100			,							metric_type=faiss.METRIC_INNER_PRODUCT			,							)
									snake_case_     ,snake_case_		     =							dset.get_nearest_examples('''vecs'''			,							np.ones(5			,							dtype=np.floataa   )   )
									self.assertEqual(examples['''filename'''][0]			,							'''my_name-train_29'''   )


				def     A_					(						self       :   Optional[Any]   ):
									import faiss

									snake_case_		     =							self._create_dummy_dataset()
									dset.add_faiss_index_from_external_arrays(
									    external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1			,							1   )			,							index_name='''vecs'''			,							metric_type=faiss.METRIC_INNER_PRODUCT			,							)

									# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
									# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
									# see https://bugs.python.org/issue14243 and
									# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
									with tempfile.NamedTemporaryFile(delete=lowerCAmelCase__   ) as tmp_file:
														dset.save_faiss_index('''vecs'''			,							tmp_file.name   )
														dset.load_faiss_index('''vecs2'''			,							tmp_file.name   )
									os.unlink(tmp_file.name   )

									snake_case_     ,snake_case_		     =							dset.get_nearest_examples('''vecs2'''			,							np.ones(5			,							dtype=np.floataa   )   )
									self.assertEqual(examples['''filename'''][0]			,							'''my_name-train_29'''   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self._create_dummy_dataset()
									dset.add_faiss_index_from_external_arrays(
									    external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1			,							1   )			,							index_name='''vecs'''   )
									dset.drop_index('''vecs'''   )
									self.assertRaises(lowerCAmelCase__			,							partial(dset.get_nearest_examples			,							'''vecs2'''			,							np.ones(5			,							dtype=np.floataa   )   )   )


				def     A_					(						self       :   Tuple   ):
									from elasticsearch import Elasticsearch

									snake_case_		     =							self._create_dummy_dataset()
									with patch('''elasticsearch.Elasticsearch.search'''   ) as mocked_search, patch(
									    '''elasticsearch.client.IndicesClient.create'''   ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''   ) as mocked_bulk:
														snake_case_		     =							{'''acknowledged''': True}
														mocked_bulk.return_value([(True, None)] * 30   )
														snake_case_		     =							{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
														snake_case_		     =							Elasticsearch()

														dset.add_elasticsearch_index('''filename'''			,							es_client=lowerCAmelCase__   )
														snake_case_     ,snake_case_		     =							dset.get_nearest_examples('''filename'''			,							'''my_name-train_29'''   )
														self.assertEqual(examples['''filename'''][0]			,							'''my_name-train_29'''   )







@require_faiss
class 		a							(  _UpperCAmelCase	):


				def     A_					(						self       :   Any   ):
									import faiss

									snake_case_		     =							FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )

									# add vectors
									index.add_vectors(np.eye(5			,							dtype=np.floataa   )   )
									self.assertIsNotNone(index.faiss_index   )
									self.assertEqual(index.faiss_index.ntotal			,							5   )
									index.add_vectors(np.zeros((5, 5)			,							dtype=np.floataa   )   )
									self.assertEqual(index.faiss_index.ntotal			,							10   )

									# single query
									snake_case_		     =							np.zeros(5			,							dtype=np.floataa   )
									snake_case_		     =							1
									snake_case_     ,snake_case_		     =							index.search(lowerCAmelCase__   )
									self.assertRaises(lowerCAmelCase__			,							index.search			,							query.reshape(-1			,							1   )   )
									self.assertGreater(scores[0]			,							0   )
									self.assertEqual(indices[0]			,							1   )

									# batched queries
									snake_case_		     =							np.eye(5			,							dtype=np.floataa   )[::-1]
									snake_case_     ,snake_case_		     =							index.search_batch(lowerCAmelCase__   )
									self.assertRaises(lowerCAmelCase__			,							index.search_batch			,							queries[0]   )
									snake_case_		     =							[scores[0] for scores in total_scores]
									snake_case_		     =							[indices[0] for indices in total_indices]
									self.assertGreater(np.min(lowerCAmelCase__   )			,							0   )
									self.assertListEqual([4, 3, 2, 1, 0]			,							lowerCAmelCase__   )


				def     A_					(						self       :   List[str]   ):
									import faiss

									snake_case_		     =							FaissIndex(string_factory='''Flat'''   )
									index.add_vectors(np.eye(5			,							dtype=np.floataa   )   )
									self.assertIsInstance(index.faiss_index			,							faiss.IndexFlat   )
									snake_case_		     =							FaissIndex(string_factory='''LSH'''   )
									index.add_vectors(np.eye(5			,							dtype=np.floataa   )   )
									self.assertIsInstance(index.faiss_index			,							faiss.IndexLSH   )
									with self.assertRaises(lowerCAmelCase__   ):
														snake_case_		     =							FaissIndex(string_factory='''Flat'''			,							custom_index=faiss.IndexFlat(5   )   )


				def     A_					(						self       :   Any   ):
									import faiss

									snake_case_		     =							faiss.IndexFlat(5   )
									snake_case_		     =							FaissIndex(custom_index=lowerCAmelCase__   )
									index.add_vectors(np.eye(5			,							dtype=np.floataa   )   )
									self.assertIsInstance(index.faiss_index			,							faiss.IndexFlat   )


				def     A_					(						self       :   List[Any]   ):
									import faiss

									snake_case_		     =							FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )
									index.add_vectors(np.eye(5			,							dtype=np.floataa   )   )

									# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
									# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
									# see https://bugs.python.org/issue14243 and
									# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
									with tempfile.NamedTemporaryFile(delete=lowerCAmelCase__   ) as tmp_file:
														index.save(tmp_file.name   )
														snake_case_		     =							FaissIndex.load(tmp_file.name   )
									os.unlink(tmp_file.name   )

									snake_case_		     =							np.zeros(5			,							dtype=np.floataa   )
									snake_case_		     =							1
									snake_case_     ,snake_case_		     =							index.search(lowerCAmelCase__   )
									self.assertGreater(scores[0]			,							0   )
									self.assertEqual(indices[0]			,							1   )







@require_faiss
def 						__magic_name__       (      __UpperCAmelCase      )						->   Union[str, Any]:

					'''simple docstring'''

					import faiss

					snake_case_		     =							FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT      )
					index.add_vectors(np.eye(5, dtype=np.floataa      )      )

					snake_case_		     =							'''index.faiss'''
					snake_case_		     =							F"mock://{index_name}"
					index.save(__UpperCAmelCase, storage_options=mockfs.storage_options      )
					snake_case_		     =							FaissIndex.load(__UpperCAmelCase, storage_options=mockfs.storage_options      )

					snake_case_		     =							np.zeros(5, dtype=np.floataa      )
					snake_case_		     =							1
					snake_case_     ,snake_case_		     =							index.search(__UpperCAmelCase      )
					assert scores[0] > 0
					assert indices[0] == 1






@require_elasticsearch
class 		a							(  _UpperCAmelCase	):


				def     A_					(						self       :   str   ):
									from elasticsearch import Elasticsearch

									with patch('''elasticsearch.Elasticsearch.search'''   ) as mocked_search, patch(
									    '''elasticsearch.client.IndicesClient.create'''   ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''   ) as mocked_bulk:
														snake_case_		     =							Elasticsearch()
														snake_case_		     =							{'''acknowledged''': True}
														snake_case_		     =							ElasticSearchIndex(es_client=lowerCAmelCase__   )
														mocked_bulk.return_value([(True, None)] * 3   )
														index.add_documents(['''foo''', '''bar''', '''foobar''']   )

														# single query
														snake_case_		     =							'''foo'''
														snake_case_		     =							{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
														snake_case_     ,snake_case_		     =							index.search(lowerCAmelCase__   )
														self.assertEqual(scores[0]			,							1   )
														self.assertEqual(indices[0]			,							0   )

														# single query with timeout
														snake_case_		     =							'''foo'''
														snake_case_		     =							{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
														snake_case_     ,snake_case_		     =							index.search(lowerCAmelCase__			,							request_timeout=30   )
														self.assertEqual(scores[0]			,							1   )
														self.assertEqual(indices[0]			,							0   )

														# batched queries
														snake_case_		     =							['''foo''', '''bar''', '''foobar''']
														snake_case_		     =							{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
														snake_case_     ,snake_case_		     =							index.search_batch(lowerCAmelCase__   )
														snake_case_		     =							[scores[0] for scores in total_scores]
														snake_case_		     =							[indices[0] for indices in total_indices]
														self.assertGreater(np.min(lowerCAmelCase__   )			,							0   )
														self.assertListEqual([1, 1, 1]			,							lowerCAmelCase__   )

														# batched queries with timeout
														snake_case_		     =							['''foo''', '''bar''', '''foobar''']
														snake_case_		     =							{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
														snake_case_     ,snake_case_		     =							index.search_batch(lowerCAmelCase__			,							request_timeout=30   )
														snake_case_		     =							[scores[0] for scores in total_scores]
														snake_case_		     =							[indices[0] for indices in total_indices]
														self.assertGreater(np.min(lowerCAmelCase__   )			,							0   )
														self.assertListEqual([1, 1, 1]			,							lowerCAmelCase__   )


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Dict         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowercase					     =				[144, 192, 240]
								__lowercase					     =				[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowercase					     =				[96, 120, 144]
								__lowercase					     =				[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowercase					     =				[64, 80, 96]
								__lowercase					     =				[16, 16, 24, 48, 64, 80, 320]
								__lowercase					     =				0.05
								__lowercase					     =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				512
								__lowercase					     =				16
								__lowercase					     =				21
								__lowercase					     =				'''pascal-voc-id2label.json'''
				else:
								__lowercase					     =				1000
								__lowercase					     =				'''imagenet-1k-id2label.json'''

				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				for i in range(1				,     6				):
								if F"layer_{i}." in name:
												__lowercase					     =				name.replace(F"layer_{i}."				,     F"encoder.layer.{i - 1}."				)

				if "conv_1." in name:
								__lowercase					     =				name.replace('''conv_1.'''				,     '''conv_stem.'''				)
				if ".block." in name:
								__lowercase					     =				name.replace('''.block.'''				,     '''.'''				)

				if "exp_1x1" in name:
								__lowercase					     =				name.replace('''exp_1x1'''				,     '''expand_1x1'''				)
				if "red_1x1" in name:
								__lowercase					     =				name.replace('''red_1x1'''				,     '''reduce_1x1'''				)
				if ".local_rep.conv_3x3." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_3x3.'''				,     '''.conv_kxk.'''				)
				if ".local_rep.conv_1x1." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_1x1.'''				,     '''.conv_1x1.'''				)
				if ".norm." in name:
								__lowercase					     =				name.replace('''.norm.'''				,     '''.normalization.'''				)
				if ".conv." in name:
								__lowercase					     =				name.replace('''.conv.'''				,     '''.convolution.'''				)
				if ".conv_proj." in name:
								__lowercase					     =				name.replace('''.conv_proj.'''				,     '''.conv_projection.'''				)

				for i in range(0				,     2				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}.layer.{j}."				)

				for i in range(2				,     6				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}."				)
																if "expand_1x1" in name:
																				__lowercase					     =				name.replace('''expand_1x1'''				,     '''downsampling_layer.expand_1x1'''				)
																if "conv_3x3" in name:
																				__lowercase					     =				name.replace('''conv_3x3'''				,     '''downsampling_layer.conv_3x3'''				)
																if "reduce_1x1" in name:
																				__lowercase					     =				name.replace('''reduce_1x1'''				,     '''downsampling_layer.reduce_1x1'''				)

				for i in range(2				,     5				):
								if F".global_rep.{i}.weight" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.weight"				,     '''.layernorm.weight'''				)
								if F".global_rep.{i}.bias" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.bias"				,     '''.layernorm.bias'''				)

				if ".global_rep." in name:
								__lowercase					     =				name.replace('''.global_rep.'''				,     '''.transformer.'''				)
				if ".pre_norm_mha.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.0.'''				,     '''.layernorm_before.'''				)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.1.out_proj.'''				,     '''.attention.output.dense.'''				)
				if ".pre_norm_ffn.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.0.'''				,     '''.layernorm_after.'''				)
				if ".pre_norm_ffn.1." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.1.'''				,     '''.intermediate.dense.'''				)
				if ".pre_norm_ffn.4." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.4.'''				,     '''.output.dense.'''				)
				if ".transformer." in name:
								__lowercase					     =				name.replace('''.transformer.'''				,     '''.transformer.layer.'''				)

				if ".aspp_layer." in name:
								__lowercase					     =				name.replace('''.aspp_layer.'''				,     '''.'''				)
				if ".aspp_pool." in name:
								__lowercase					     =				name.replace('''.aspp_pool.'''				,     '''.'''				)
				if "seg_head." in name:
								__lowercase					     =				name.replace('''seg_head.'''				,     '''segmentation_head.'''				)
				if "segmentation_head.classifier.classifier." in name:
								__lowercase					     =				name.replace('''segmentation_head.classifier.classifier.'''				,     '''segmentation_head.classifier.'''				)

				if "classifier.fc." in name:
								__lowercase					     =				name.replace('''classifier.fc.'''				,     '''classifier.'''				)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowercase					     =				'''mobilevit.''' + name

				return name





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				if base_model:
								__lowercase					     =				''''''
				else:
								__lowercase					     =				'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowercase					     =				orig_state_dict.pop(lowercase				)

								if key[:8] == "encoder.":
												__lowercase					     =				key[8:]

								if "qkv" in key:
												__lowercase					     =				key.split('''.'''				)
												__lowercase					     =				int(key_split[0][6:]				) - 1
												__lowercase					     =				int(key_split[3]				)
												__lowercase					     =				model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"				)
												__lowercase					     =				layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__lowercase					     =				(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__lowercase					     =				val[:dim, :]
																__lowercase					     =				val[dim : dim * 2, :]
																__lowercase					     =				val[-dim:, :]
												else:
																__lowercase					     =				val[:dim]
																__lowercase					     =				val[dim : dim * 2]
																__lowercase					     =				val[-dim:]
								else:
												__lowercase					     =				val

				return orig_state_dict





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				get_mobilevit_config(lowercase				)

				# load original state_dict
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				MobileViTForSemanticSegmentation(lowercase				).eval()
				else:
								__lowercase					     =				MobileViTForImageClassification(lowercase				).eval()

				__lowercase					     =				convert_state_dict(lowercase				,     lowercase				)
				model.load_state_dict(lowercase				)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowercase					     =				MobileViTImageProcessor(crop_size=config.image_size				,     size=config.image_size + 32				)
				__lowercase					     =				image_processor(images=prepare_img()				,     return_tensors='''pt'''				)
				__lowercase					     =				model(**lowercase				)
				__lowercase					     =				outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''				):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowercase					     =				torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3, :3, :3]				,     lowercase				,     atol=1E-4				)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowercase					     =				torch.tensor([-0.9866, 0.2392, -1.1241]				)
								elif mobilevit_name == "mobilevit_xs":
												__lowercase					     =				torch.tensor([-2.4761, -0.9399, -1.9587]				)
								elif mobilevit_name == "mobilevit_xxs":
												__lowercase					     =				torch.tensor([-1.9364, -1.2327, -0.4653]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3]				,     lowercase				,     atol=1E-4				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)
				print(F"Saving image processor to {pytorch_dump_folder_path}"				)
				image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								__lowercase					     =				{
								    '''mobilevit_s''': '''mobilevit-small''',
								    '''mobilevit_xs''': '''mobilevit-x-small''',
								    '''mobilevit_xxs''': '''mobilevit-xx-small''',
								    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
								    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
								    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
								}

								print('''Pushing to the hub...'''				)
								__lowercase					     =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowercase				,     organization='''apple'''				)
								model.push_to_hub(lowercase				,     organization='''apple'''				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--mobilevit_name""",
		    default="""mobilevit_s""",
		    type=str,
		    help=(
		        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
		        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
		    ),
		)
		parser.add_argument(
		    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	Dict         =    parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)